Search: “safety”

Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media

arXiv:2609.14256v1 Announce Type: new Abstract: Topic models are widely used to analyze public health-related social media short texts, yet their evaluation remains dominated by metrics that focus ent

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Does Online Gravity Estimation Matter? Revisiting a Silent Design Split in LiDAR-Inertial Odometry

DGX agent

arXiv:2609.13675v1 Announce Type: new Abstract: LiDAR-inertial odometry (LIO) systems differ in whether they continue estimating gravity after initialization. We compare four gravity-bias state config

safetyarxiv-cs-ro

15 Sept 2026

Dream-RSI: Recursive Self-Improvement through Evolving Worlds

arXiv:2609.14858v1 Announce Type: new Abstract: Recursive self-improvement is becoming increasingly vital for autonomous AI agents, where progress hinges on discovering high-value solutions across com

DuoTok: Source-Aware Dual-Track Music Tokenization for Vocal-Accompaniment Generation

arXiv:2511.20224v3 Announce Type: replace-cross Abstract: Multi-track music generation requires tokens that preserve acoustic fidelity, support sequence modeling, and maintain cross-track structure. R

Dynamics-Informed Reinforcement Learning for Agile and Energy-Efficient Locomotion of a Monopedal Hopping Quadcopter

arXiv:2609.15399v1 Announce Type: new Abstract: Although aerial-legged robots offer combined agility and efficiency, controlling high-speed hopping under complex hybrid dynamics is challenging. Reinfo

ECAS: An Edge-Controlled Agentic System for Validation-Gated Scientific Application Execution

arXiv:2609.14211v1 Announce Type: cross Abstract: Scientific applications increasingly rely on high-performance computing (HPC), yet translating a scientist's high-level goal into a correct target-sca

EMoG: Emotion-Modulated Gait Generation for Expressive Humanoid Locomotion

arXiv:2609.14432v1 Announce Type: new Abstract: Existing humanoid locomotion systems primarily focus on stability and task execution, while integrating expressiveness with explicit locomotion control

Equilibrium bias and convergence in augmented primal--dual dynamics with sampled constraints

arXiv:2609.13925v1 Announce Type: cross Abstract: This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples. Unbiased constrai

Evaluating covariate balance for long time horizon Markov decision processes

arXiv:2607.15080v2 Announce Type: replace Abstract: This article explores the application of covariate balance diagnostics for detecting the presence of hidden confounding/model miss-specification in

EventVL: Understand Event Streams via Multimodal Large Language Model

arXiv:2501.13707v3 Announce Type: replace-cross Abstract: The event-based Vision-Language Model (VLM) recently has made good progress for practical vision tasks. However, most of these works just util

Extending the Speed Limit of Quadrupedal Locomotion via Refined Actuator Modeling and Adaptive Command Scheduling

arXiv:2609.13289v1 Announce Type: new Abstract: Achieving high-speed locomotion in quadrupedal robots remains highly challenging, as actuators operate near their physical limits and exhibit pronounced

Flow-Matched Motion Priors: Online Optimal-Transport Rewards for Imitation Learning

arXiv:2609.15631v1 Announce Type: new Abstract: Learning a motion prior requires a reward that guides a policy from its current behavior toward demonstrated motion. Adversarial Motion Priors (AMP) pro

Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation

arXiv:2609.13779v1 Announce Type: new Abstract: Force-controlled loco-manipulation requires a whole-body policy to coordinate locomotion and arm motion while regulating end-effector interaction forces

Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning

arXiv:2609.14896v1 Announce Type: cross Abstract: A notable byproduct of LLM alignment training is mode collapse: the progressive loss of output diversity that narrows a model's expressivity at infere

From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements

arXiv:2609.13535v1 Announce Type: new Abstract: The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy

From Network Inequality to Network Fairness: A Perspective on Responsible Decision-Making

arXiv:2609.13867v1 Announce Type: cross Abstract: Social networks shape how individuals make decisions and how opportunities are distributed. However, the mechanisms that generate these networks often

From Prediction to Decision: World-Model-Guided Action Selection for Continuous Pile Excavation

arXiv:2609.15382v1 Announce Type: new Abstract: Wheel-loader excavation is a sequential decision problem in which every scoop changes the terrain available to subsequent actions. A practical world mod

full support this proposal from @DKThomp, and there should be a similar broadening for negligent defamation

full support this proposal from @DKThomp, and there should be a similar broadening for negligent defamation Drop Site and parts of the antitrust left are poo-poohing the idea that we need new rules to

safetygary-marcus--x

GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction

arXiv:2609.13778v1 Announce Type: cross Abstract: Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substan

Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement

arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intellig

Generate to Explore, Select to Exploit: Aligning LLM-based Headline Generation with Personalized Recommendation

arXiv:2609.15094v1 Announce Type: cross Abstract: In industrial recommendation feeds, presenting a static headline for an item often fails to satisfy the diverse, multimodal interests of the user popu

GeoTTER: Leveraging Local Geometry of Optimal Transport for Zero-Shot Classification

arXiv:2609.13518v1 Announce Type: new Abstract: We present GeoTTER, a novel framework that redefines optimal transport in the realm of zero-shot classification. Conventional methods often suffer from

GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors

arXiv:2609.14173v1 Announce Type: new Abstract: Human-to-robot skill transfer from sensing gloves has so far relied on shared hardware: the same tactile glove worn by the demonstrator and the robot, o

Governing at Machine Speed: An Adaptive Intelligence Architecture for Real-Time AI Policy Enforcement

arXiv:2609.13466v1 Announce Type: new Abstract: Enterprise AI adoption has reached 78% of organizations globally, yet the infrastructure to govern that adoption has not kept pace. This paper identifie

GraMRAG: Orchestrating Multi-Agent Multi-Step Reasoning via Graph Memory with Reinforcement Learning

arXiv:2609.14066v1 Announce Type: cross Abstract: Although existing multi-agent Retrieval-Augmented Generation (RAG) systems have demonstrated promise on complex multimodal reasoning tasks, they remai

Graph Matching Relaxations and Amortization for Supervised Graph Prediction

arXiv:2609.15437v1 Announce Type: cross Abstract: End-to-end Supervised Graph Prediction (SGP) requires a permutation-invariant loss to compare predicted and target graphs with arbitrary node ordering

GRPO-QM: Target Preserving Exploration for Quantum Tomography

arXiv:2609.14711v1 Announce Type: new Abstract: Reward-based learning can alter the very posterior distribution that scientific inference aims to estimate. GRPO-QM sidesteps this by learning only an e

HarnessBandit: Joint Learnability-Transferability Scheduling for Multi-Harness Agentic Reinforcement Learning

arXiv:2609.13739v1 Announce Type: cross Abstract: Language-model agents are increasingly deployed through diverse harnesses that differ in system prompts, tool schemas, control loops, and trajectory f

Harnessing human expertise for high-precision robotic assembly in industrialized construction: A sample-efficient installer-in-the-loop interactive reinforcement learning framework

arXiv:2609.13234v1 Announce Type: new Abstract: Industrialized construction imposes stringent precision requirements on robotic assembly of modular components such as prefabricated window units. In to

Harnessing Image Question Dependence for Better VLM Test-time Reinforcement Learning

arXiv:2609.13296v1 Announce Type: new Abstract: Test-time reinforcement learning can adapt vision-language models (VLMs) to unlabeled target data, but its effectiveness is fundamentally limited by the

safetyarxiv-cs-cv

High-Probability Nash Regret for Decentralized Learning in Markov alpha-Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games

arXiv:2609.14959v1 Announce Type: new Abstract: We study decentralized learning of Nash equilibria (NE) in infinite-horizon discounted Markov games under bandit feedback, focusing on Markov alpha-pote

Hindsight-Anchored Policy Optimization: Learning Through Hindsight with Thompson Sampling-Inspired Adaptive Gating

arXiv:2603.11321v3 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards improves reasoning in large language models, yet on-policy learning often suffers from cold-sta

HISPO: Hierarchical Importance-Sampling Policy Optimization with Entropy-Derived Segments

arXiv:2609.15471v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a central approach for improving mathematical reasoning in language models, but long-fo

Homeostatic Continual Learning

arXiv:2609.13771v1 Announce Type: new Abstract: In this paper, I formulate a Continual Learning problem and propose a method named 'Homeostatic Continual Learning' that enables an AI agent to learn co

Horizon-specific Expert Fusion for Photovoltaic Power Forecasting

arXiv:2609.15035v1 Announce Type: new Abstract: Short-term photovoltaic power forecasting requires models to represent regular solar cycles and weather-driven fluctuations whose importance changes wit

How often do AI agents cheat? We’re releasing CheatBench, a reward gaming evaluation spanning math, coding, knowledge work, visual tasks, an…

How often do AI agents cheat? We’re releasing CheatBench, a reward gaming evaluation spanning math, coding, knowledge work, visual tasks, and more. After Hugging Face, AI companies tried to address th

safetydan-hendrycks--x

Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval

arXiv:2609.15320v1 Announce Type: new Abstract: Volume-based multimodal retrieval jointly scores a text query with a candidate's video, audio, and subtitle embeddings. While this approach captures hig

safetyarxiv-cs-cv

HyperProve: Answer-Guided Hypergraph Expansion for Multi-Hop Question Answering

arXiv:2609.13768v1 Announce Type: new Abstract: Multi-hop question answering often fails when retrieval treats evidence as isolated matches to the original question, since the facts needed to answer a

IMPACT-VLA: Interaction-aware Multimodal Propagation Attribution via Counterfactual Trajectories for Vision-Language-Action Policies

arXiv:2609.15005v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies perform robot manipulation tasks using multimodal inputs such as visual observations, proprioceptive states, and

Isolation-based Spherical Ensemble Representations for Tabular Anomaly Detection

arXiv:2510.13311v2 Announce Type: replace Abstract: Unsupervised tabular anomaly detection is a critical task with applications spanning offensive language detection, network security, and quality con

Jensen Huang took a surprise call from President Trump onstage at the All-In Summit on Monday in LA, where the two dismissed fears of an AI takeover (New York Times)

New York Times: Jensen Huang took a surprise call from President Trump onstage at the All-In Summit on Monday in LA, where the two dismissed fears of an AI takeover — Mr. Trump called Jensen Huang, Nv

safetytechmeme

JEPLO: Joint-Embedding Predictive Learning for LiDAR-Based Legged Locomotion

arXiv:2609.15770v1 Announce Type: new Abstract: Light detection and ranging (LiDAR) remains less explored than RGB-D sensing for perceptive legged locomotion, and existing LiDAR-based approaches often

JSolver: Joint Spectrum Estimation and Multi-Material Decomposition from Single-Energy CT Projections

arXiv:2505.08123v2 Announce Type: replace-cross Abstract: Multi-material decomposition (MMD) enables quantitative reconstruction of tissue compositions in the human body, supporting a wide range of cl

Just add noise: Debiasing tree-based variable importance in mixed data

arXiv:2609.14083v1 Announce Type: cross Abstract: Variable importance scores from tree-based methods such as random forests favor continuous predictors over categorical ones. We present a theoretical

Know When to Stop, Where to Restart: Accelerating Multi-Turn Agentic On-Policy Distillation

arXiv:2609.14636v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a standard approach for transferring capabilities from large teachers to compact students. Its cost, however,

Language-Grounded Semantic Target Navigation for Autonomous Surface Vehicles

arXiv:2609.14558v1 Announce Type: new Abstract: Autonomous Surface Vehicles (ASVs) are increasingly expected to operate in ports and harbour environments, where operators may specify navigation target

Learning Manipulation-Sufficient Representations via Outcome Bottlenecks

arXiv:2609.13235v1 Announce Type: cross Abstract: Networked manipulation endpoints couple perception to actuation across compute- and bandwidth-limited links, yet commonly exchange dense geometric sta

safetyarxiv-cs-cv

Learning Multimodal One-step Flow Policy via Value-weighted Optimal Transport

arXiv:2609.15883v1 Announce Type: cross Abstract: Offline reinforcement learning aims to learn a policy solely from fixed datasets, which often contain multimodal action distributions. Flow policies c